Description Usage Arguments Details Value Examples

Do conditional independence test on DDDataSet

1 2 |

`obj` |
DDDataSet object on which (conditional) independence test needs to be done |

`var1` |
the name or index of the first variable to be tested |

`var2` |
the name or index of the second variable |

`cond` |
the names or indexes of variables to condition on (defaults to NULL) |

`test.type` |
the type of statistical test (defaults to mc-x2) |

`B` |
the number of replicates for MC-based tests (default to NULL) |

`min.table.size` |
the minimal number of samples in a contingency table per conditioning set (makes sense only for discrete data) |

`...` |
unused |

This function does a conditional independence var1 indep var2 | cond. The following
test types are available (implemented by package `bnlearn`

).

For binary data:

"fisher" - Fisher's exact test (only for unconditional independence)

"mi" - Mutual Information (discrete)

"mi-sh" - Mutual Information (discrete, shrinkage)

"mc-mi" - Mutual Information (discrete, Monte Carlo)

"aict" - AIC-like Test

"x2" - Pearson's X^2

"mc-x2" - Pearson's X^2 (Monte Carlo)

"mc-x2-c" - Pearson's X^2 (Monte Carlo) the corrected version

"g2" - G^2 test (requires pcalg package)

For continuous data:

"mi-g" - Mutual Information (Gaussian)

"mi-g-sh" - Mutual Information (Gaussian, shrinkage)

"mc-mi-g" - Mutual Information (Gaussian, Monte Carlo)

"cor" - Pearson's Linear Correlation

"mc-cor" - Pearson's Linear Correlation (Monte Carlo)

"zf" - Fisher's Z Test

"mc-zf" - Fisher's Z Test (Monte Carlo)

CITestResult object with the result of the test

1 2 3 4 5 6 7 | ```
data(mesoBin)
# test if tin_4.6 is independent of class labels
ciTest(mesoBin$Meso, "Tin 4-6h", "class")
# test if tin_4.6 is independent of class conditioned on twi_2.4
ciTest(mesoBin$Meso, "Tin 4-6h", "class", "Twi 2-4h")
# repeat the test using G2 asymptotic distribution
ciTest(mesoBin$Meso, "Tin 4-6h", "class", "Twi 2-4h", test.type="g2")
``` |

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